Using extracted image text

ABSTRACT

Methods, systems, and apparatus including computer program products for using extracted image text are provided. In one implementation, a computer-implemented method is provided. The method includes receiving an input of one or more image search terms and identifying keywords from the received one or more image search terms. The method also includes searching a collection of keywords including keywords extracted from image text, retrieving an image associated with extracted image text corresponding to one or more of the image search terms, and presenting the image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of, and claims priorityto U.S. patent application Ser. No. 14/291,331, filed on May 30, 2014,which is a continuation application of, and claims priority to, U.S.patent application Ser. No. 13/620,944, now U.S. Pat. No. 8,744,173,filed on Sep. 15, 2012, which is a continuation application of, andclaims priority to, U.S. patent application Ser. No. 13/350,726, nowU.S. Pat. No. 8,503,782, filed on Jan. 13, 2012 entitled, “USINGEXTRACTED IMAGE TEXT,” which is a divisional application of, and claimspriority to, U.S. patent application Ser. No. 11/479,155, now U.S. Pat.No. 8,098,934, filed on Jun. 29, 2006, entitled, “USING EXTRACTED IMAGETEXT.” The disclosure of the foregoing applications is incorporatedherein by reference.

BACKGROUND

The present disclosure relates to image processing for recognizing textwithin images.

Digital images can include a wide variety of content. For example,digital images can illustrate landscapes, people, urban scenes, andother objects. Digital images often include text. Digital images can becaptured, for example, using cameras or digital video recorders.

Image text (i.e., text in an image) typically includes text of varyingsize, orientation, and typeface. Text in a digital image derived, forexample, from an urban scene (e.g., a city street scene) often providesinformation about the displayed scene or location. A typical streetscene includes, for example, text as part of street signs, buildingnames, address numbers, and window signs.

An example street scene 100 is shown in FIG. 1. Street scene 100includes textual elements such as logo text 102 on an automobile as wellas building signs 104 and 106. Text found within images can identifyaddress locations, business names, and other information associated withthe illustrated content.

The text within images can be difficult to automatically identify andrecognize due both to problems with image quality and environmentalfactors associated with the image. Low image quality is produced, forexample, by low resolution, image distortions, and compressionartefacts. Environmental factors include, for example, text distance andsize, shadowing and other contrast effects, foreground obstructions, andeffects caused by inclement weather.

SUMMARY

Systems, methods, and apparatus including computer program products fortext identification and recognition in images are described. Textrecognition and extraction from an image includes preprocessing areceived image, identifying candidate text regions within the image,enhancing the identified candidate text regions, and extracting textfrom the enhanced candidate text regions using a character recognitionprocess. For an image showing an urban scene, such as a portion of acity block, the text recognition process is used to identify, forexample, building addresses, street signs, business names, restaurantmenus, and hours of operation.

In accordance with one aspect, a computer-implemented method forrecognizing text in an image is provided. The method includes receivinga plurality of images. The method also includes processing the images todetect a corresponding set of regions of the images, each image having aregion corresponding to each other image region, as potentiallycontaining text. The method further includes combining the regions togenerate an enhanced region image and performing optical characterrecognition on the enhanced region image.

In accordance with one aspect, a computer-implemented method forrecognizing text in an image is provided. The method includes receivingan image and processing the image to divide the image into one or moreregions. The method includes detecting one or more features in eachregion and determining for each region whether it is a candidate textregion potentially containing text using the detected features. Themethod further includes enhancing the candidate text regions to generatean enhanced image and performing optical character recognition on theenhanced image

In accordance with one aspect, a system is provided. The system includesmeans for receiving a plurality of images and means for processing theimages to detect a corresponding set of regions of the images aspotentially containing text. The system also includes means forcombining the regions to generate an enhanced region image and means forperforming optical character recognition on the enhanced region image.

In accordance with one aspect, a system is provided. The system includesmeans for receiving an image and means for processing the image todivide the image into one or more regions. The system includes means fordetecting one or more features in each region and means for determiningfor each region whether it is a candidate text region potentiallycontaining text using the detected features. The system also includesmeans for enhancing the candidate text regions to generate an enhancedimage and means for performing optical character recognition on theenhanced image.

In accordance with one aspect, a method is provided. The method includesreceiving an input of one or more image search terms identifyingkeywords from the received one or more image search terms. The methodincludes searching a collection of keywords including keywords extractedfrom image text. The method further includes retrieving an imageassociated with extracted image text matching a search term andpresenting the image.

In accordance with one aspect, a method is provided. The method includesreceiving an image including data identifying a location associated withthe image and extracting text from within the image. The method includesindexing the extracted text and receiving a request and using theextracted text to determine that the image satisfies the request. Themethod further includes presenting information including the image to auser in response to the request.

In accordance with one aspect, a system is provided. The system includesmeans for receiving an input of one or more image search terms and meansfor searching a collection of keywords including keywords extracted fromimage text. The system also includes means for retrieving an imageassociated with extracted image text matching a search term and meansfor presenting the image.

In accordance with one aspect, as system is provided. The systemincludes means for receiving an image including data identifying alocation associated with the image and a means for extracting text fromwithin the image. The system includes means for indexing the extractedtext and means for receiving a request and using the extracted text todetermine that the image satisfies the request. The system also includesmeans for presenting information including the image to a user inresponse to the request.

In accordance with another aspect, a method is provided. The methodincludes receiving a plurality of images including a version of anidentified candidate text region. The method includes aligning eachcandidate text region image from the plurality of images to a highresolution grid. The method further includes compositing the alignedcandidate text regions to create a single superresolution image andperforming character recognition on the superresolution image toidentify text.

In accordance with one aspect, a system is provided. The system includesmeans for receiving a plurality of images each including a version of anidentified candidate text region. The system includes means for aligningeach candidate text region from the plurality of images to a highresolution grid. The system also includes means for compositing thealigned candidate text regions to create a single superresolution imageand means for performing character recognition on the superresolutionimage to identify text.

Particular embodiments of the invention can be implemented to realizeone or more of the following advantages. Candidate text regions withinimages can be enhanced to improve text recognition accuracy. Extractedimage text can also be used to improve image searching. The extractedtext can be stored as associated with the particular image for use ingenerating search results in an image search. Additionally, theextracted image text can be combined with location data and indexed toimprove and enhance location-based searching. The extracted text canprovide keywords for identifying particular locations and presentingimages of the identified locations to a user.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,aspects, and advantages of the invention will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an image that includes textual elements.

FIG. 2 is a block diagram of an example text recognition system.

FIG. 3 shows an example process for recognizing text in an image.

FIG. 4A shows an image before a normalizing operation.

FIG. 4B shows the image of FIG. 4A after normalization.

FIG. 5A shows one example of detected candidate text regions for animage.

FIG. 5B shows another example of detected candidate text regions for theimage.

FIG. 6 shows an example process for generating a superresolution result.

FIG. 7A shows a set of regions, including text, extracted from multipleimages of a scene.

FIG. 7B shows a scaled up version of the text from an image shown inFIG. 7A.

FIG. 7C shows the scaled up candidate text from FIG. 7B aligned to ahigh resolution grid.

FIG. 7D shows a superresolution result.

FIG. 8A is an image including candidate text regions.

FIG. 8B shows the results of a character recognition operation for thecandidate text regions of FIG. 7A.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION Architecture

FIG. 2 is a block diagram of an example text recognition system 200. Thetext recognition system 200 includes an image component 202, an imagepreprocessing module 204, a text detection component 206, a text boxenhancement component 208, and character recognition component 210.

Image component 202 collects, stores, or otherwise manages one or moreimages for text recognition. Image component 202 can include one or moreimage databases or can retrieve images from a data store such as one ormore remote image databases. Alternatively, the image component 202 canreceive images for text recognition in realtime from a remote location,for example, as part of an image or video feed. The process ofcollecting and storing images can be automated or user driven. Theimages can be retrieved, for example, as a result of a user inputselecting one or more images for use in the text recognition process.

Image preprocessing component 204 provides an optional level of initialprocessing for the images provided by the image component 202. The imagepreprocessing enhances the images prior to text detection by the textdetection component 206. In one implementation, the image preprocessingcomponent 204 first analyzes each image to determine whether or notpreprocessing of the image is necessary. Alternatively, every image isautomatically preprocessed by the image preprocessing component 204.

Preprocessing is performed on an image, for example, when the imageincludes regions of low contrast. Photographic images, for example, aresubject to environmental conditions affecting image contrast such aschanges in lighting conditions or shadows generated by physical objects.For example, a tree in the foreground of an image can cast a shadow overa portion of text, reducing contrast between the text and thesurrounding features in the image. Additionally, or alternatively, inanother implementation, preprocessing is performed to correct imagequality problems, for example, the presence of compression artefacts.

Text detection component 206 detects candidate regions of an image thatcontains text or is likely to contain text. The text in the candidatetext regions are then identified by character recognition component 210.The text identifier component 206 includes a classifier 207 configuredto detect the presence of text within an image. The classifier istrained to detect candidate text regions using feature detection. Thecandidate text regions detected by the text detection component 206 arefurther processed by the text box enhancement component 208.

Text box enhancement component 208 enhances the candidate text regionsof the image detected by the text detection component 206. The candidatetext regions are enhanced to increase the accuracy of textidentification by the character recognition component 210. In oneimplementation, the candidate text regions are enhanced by performing asuperresolution operation to generate a single superresolution imagefrom a number of separate images. The superresolution process isdescribed below.

In another implementation, an inverse (or negative) version of eachcandidate text region is generated. The inverse version changes, forexample, white text into black text in order to improve textidentification using a character recognition application calibrated forrecognizing dark text on a light background.

Character recognition component 210 analyzes the enhanced candidate textbox regions to identify and extract text. The character recognitioncomponent 210 applies a character recognition program (e.g., an opticalcharacter recognition (“OCR”) program) to identify alphanumericcharacters within the text box regions and to extract the identifiedcharacters. Identified characters can be further processed, for example,to eliminate nonsense results generated by the character recognitionprogram in an attempt to identify text from non-text features in acandidate text region.

Text Recognition Process

FIG. 3 shows an example process 300 for recognizing text in an image.Process 300 can be initiated, for example, by a user or can be acomponent of an automated system for processing images.

Image Collection

The first step in the text recognition process 300 is to receive one ormore images (e.g., from the image component 202) (step 302). The imagescan be received from numerous sources including local storage on asingle computer or multiple computing devices distributed across anetwork. For example, the images can be retrieved from one or more,local or remote, image databases or can be collected in realtime forprocessing.

The received images may have been captured, for example, usingconventional digital cameras or video recording devices. The resultingcaptured images can include panoramic images, still images, or frames ofdigital video. The captured images can also be associated withthree-dimensional ranging data as well as location information, whichcan be used in processing the images.

An example image type is a panoramic image of a street scene. A singlepanoramic image can capture multiple street addresses (e.g., one cityblock, or a string of contiguous address locations on a street). Suchpanoramic pictures are taken, for example, using a panoramic camera or aregular camera equipped with a panoramic lens. Alternatively, apushbroom panoramic image can be generated for a street scene by merginga sequence of discrete images collected, for example, from a movingcamera.

Location data can be associated with each image. For example, the GPScoordinates at every point along a given panoramic image can be known oraccurately calculated using an appropriate technique. For example, for apanoramic picture corresponding to a block from “100” to “200” on agiven street, where the GPS location at either end of the block is known(e.g., based on GPS receiver data taken at the time of image capture),then the GPS coordinates can be calculated at every intermediate pointusing linear interpolation. Consequently, GPS coordinates can bedetermined for each corresponding location in the panoramic image.

In an alternative implementation, a set of GPS coordinates are known foreach image, corresponding to the exact location where each image wascaptured. For example, if each image corresponds to one particularstreet address, then given a series of such image/GPS data pairs, exactGPS coordinates are known for each corresponding address location onthat street.

Additionally, exact GPS coordinates of every image or vertical line inan image can be determined. For example, a differential GPS antenna on amoving vehicle can be employed, along with wheel speed sensors, inertialmeasurement unit, and other sensors, which together allow a veryaccurate GPS coordinate to be computed for each image or portions of theimage.

Image Preprocessing

The received images may need preprocessed in order to increase theprobability of detecting text within the images. For example, text in animage from a street scene can be located within a shadow (e.g., cast bya tree). The shadow results in a region of low contrast between the textand the surrounding image features. The low contrast increases thedifficulty in distinguishing the text from background featuressurrounding the text.

In one implementation, a determination is made as to whether the imagesare to be preprocessed (step 304). In making the preprocessingdetermination, the image source can be considered. For example, imagestaken of a city street may have a higher need for preprocessing thenother images taken, for example, within a store where environmental(e.g., lighting) conditions are more controlled. Similarly, highresolution images are less in need of preprocessing as compared to lowresolution images. Additionally, the source of the images can be used todetermine the particular type of preprocessing to perform on an image.For example, an image encoded in a format having fewer artefacts (e.g.,compression artefacts) may require less preprocessing. However, in analternative implementation, all images are automatically preprocessed(or not preprocessed at all) without the determination step 304, forexample, to expedite processing or because of known informationregarding a particular set of images.

Each designated image is preprocessed (e.g., using image preprocessingcomponent 204) (step 306). In one implementation, a normalizationprocess is performed on each image. Normalization of the image isperformed to enhance the contrast in the image, in particular betweenthe text and background in low-contrast regions of the image. Oneexample normalization process is adaptive gray value normalization. Inadaptive gray value normalization, a mean and variance for each pixel inthe image is computed. The pixel values are mapped to new pixel valuesaccording to a predetermined mean and standard deviation value. Aminimum standard deviation value can be selected to prevent contrastover enhancement in areas of the image having a low variance.

FIG. 4A shows an example image 400 prior to normalization. The image 400includes text 402. The text 402 is located in a region of low contrastbetween the text 402 and the region surrounding the text 402. FIG. 4Bshows a normalized image 404. The normalized image 404 represents theimage 400 following the normalization process. The text 402 in thenormalized image 404 has a greater contrast such that the text 402 ismore easily discernable from the surrounding image.

Other preprocessing operations can be performed. In one implementation,a high dynamic range process is performed (instead of, or in additionto, normalization) to preprocess the images. Multiple exposures of animage are used in the high dynamic range processes to create a highdynamic range image. For example, three exposures ranging from bright tomedium to dark exposure can be captured by a camera. To create the highdynamic range image, the three exposures are composited to create asingle image. Like normalization, the high dynamic range process alsoprovides an image with enhanced contrast, including text regions, whichincreases the ability to distinguish the text from the surroundingbackground features.

The images can also be processed to correct for various imagedistortions. For example, the images can be processed to correct forperspective distortion. Text positioned on a plane that is notperpendicular to the camera is subject to perspective distortion, whichcan make text identification more difficult. Conventional perspectivedistortion correction techniques can be applied to the images duringpreprocessing.

Text Detection

The images are processed for text detection (e.g., using text detectioncomponent 206) (step 308). During text detection processing, candidatetext regions of the image are detected as possibly containing text. Aclassifier is used to detect the candidate text regions. An existing ornew classifier is trained to identify features in an image thatindicate, within some degree of confidence, the presence of text. A setof sample text and non-text patterns is used to train the classifier.The classifier is trained to distinguish between text and non-text imagefeatures based on the set of sample patterns. To increase the accuracyof the classifier, the set of sample patterns used to train theclassifier corresponds to images similar to those to be examined. Forexample, sample patterns derived from images of city street scenes areused when the classifier is being trained to identify candidate textregions in images showing city streets. Different training sets of textand non-text patters can be used when training the classifier to detecttext in different types of images. For example, when using a classifierto detect text in images of consumer items located within a store,detect images cataloging museum object, or to detect text in anothertype of image collection (including personal image collections),different training sets of patterns are used so that the classifier iscalibrated to identify text present in that type of image.

The classifier distinguishes between text and non-text in images byanalyzing features or combinations of features within the image. Anumber of different features types can be examined by the classifier fordetecting text in the image. Typically, the image is divided into anumber of smaller image sub-regions (e.g., squares of 16×16 pixels,rectangles of 40×20 pixels, disks having a 10 pixel radius, etc.), whichare then individually processed for feature analysis. The sub-regionscan overlap (e.g., by 5 pixels) to increase accuracy of the textdetection. For example, two neighboring sub-regions can have 40% ofpixels in common.

Extracted features characterize different properties of the image suchas line segment properties (e.g., the shape or orientation of linesegments) as well as other features such as color or gradients. Table 1shows a list of feature types which are used by the classifier in oneimplementation. The classifier is not limited to the features describedbelow, which are illustrative. The results from analyzing one or morefeatures in a particular image sub-region provide an indication as towhether or not the examined image sub-region contains text.

TABLE 1 Type The horizontal derivative and its mean in a local, 0box-shaped surrounding are used as a feature. Type The verticalderivative and its mean in a local, 1 box-shaped surrounding are used asa feature. Type The horizontal derivative and its variance in a 2 local,box-shaped surrounding are used as a feature. Type The verticalderivative and its variance in a local, 3 box-shaped surrounding areused as a feature. Type A joint 2-dimensional histogram over abox-shaped 4 surrounding where dimension one is image intensity anddimension two is the gradient strength. Type The distribution of cannyedgels (edge elements 5 found using a Canny edge detector) over fourorientations in a local, box-shaped surrounding. Type A 1-dimensionalhistogram of the gradient strength. 6 Type Corners: A measure forstrength of corners in a 7 local box-shaped surrounding is used as afeature. Therefore, the minimum eigenvalue image computed by a cornerdetector (e.g., a Harris Corner operator or Kanade-Lucas-Tomasioperator, which detects corners using a local structure matrix) is used,and its local mean is computed as a feature. Type The vertical andhorizontal projection profiles in 8 a box-shaped surrounding are used asa feature. Extract their variance (or the mean of their derivative).

The classifier is run for each image sub-region and according to thefeature analysis a text/no text determination is made for each imagesub-region. Adjacent image sub-regions with detected text are combinedto form candidate text regions for the image.

In one implementation, the classifier is calibrated to identify featurescorresponding to text within a particular text size range. If theclassifier is trained to detect text of a particular size, the inputimage is scaled across a range of steps. The classifier performs textdetection at each scaled step searching for text at the trained height.Consequently, a set of scaled images are created for each image (i.e., apyramid of scaled images) such that the classifier is run multiple timesfor each image in order to detect differently sized text.

The results for adjacent scale steps can be used to eliminate falsepositive candidate text regions. The amount of scaling for each step ischosen so that the same candidate text region is detected at more thanone step level in the image set (i.e., a stable text region). In otherwords, the scale step is selected such that the classifier is capable ofdetecting text at adjacent scale steps. If text is not identified atadjacent scale step, the detection at only one step size is likely afalse positive result. Consequently, false positives in the textdetection can be reduced by requiring a candidate text region to appearin at least two adjacent scale steps.

Additionally, in one implementation, a minimum size requirement isapplied to detected candidate text regions (e.g., the collection ofadjacent image regions where the classifier detected text). The minimumsize requirement allows for small candidate text regions providing falsepositive results to the text detector to be eliminated. However, if theminimum size requirement is set too large some valid text will not bedetected (false negative results).

FIG. 5A shows one example of detected candidate text regions for animage 500 where the classifier has a first minimum size requirement fordetected candidate text regions. Image 500 shows a street sceneincluding a building entrance. Within image 500 are a number of detectedcandidate text regions 502 and 504. The detected candidate text regionsrepresent areas of the image that the classifier determined aspotentially having text. As shown in FIG. 5A, the image 500 includescandidate text region 502, which includes the building number “155”above the door of the building. Image 500 also includes candidate textregions 504 representing false positive regions identified as havingtext.

The number of false positive candidate text regions can be reduced byincreasing the minimum size requirement for candidate text regionsdetected by the classifier. However, increasing the minimum size canalso lead to failure in detecting text (i.e., an increased probabilityof false negatives). FIG. 5B shows an example of the detected candidatetext regions for the same image 500 when a larger minimum sizerequirement is used for detecting candidate text regions. In image 500,fewer candidate text regions 506 have been detected by the classifier.However, the building number “155” is smaller than the minimum candidatetext region size and therefore has not been detected. Thus, a particularminimum size requirement for candidate text regions should be selectedto minimize false negative results without excessive false positives.

In one implementation, three-dimensional range data associated with animage is used to eliminate false positive candidate text regions. Duringimage collection, range sensors can be used to gather three-dimensionalrange data for each captured image. For example, the range data can beprovided by range sensors such as laser range sensors (e.g., laserdetection and ranging (“LIDAR”) devices) or stereo-based sensors (i.e.,stereoscopic imaging devices) located in proximity to the imagecapturing device. The three-dimensional range data provides informationregarding the distance from the camera position to points in the image.For example, the distance from the camera to a building door or thedistance to a foreground object such as a tree or signpost.

The three dimensional range data for points in the image are used todecompose the image into planar and non-planar regions. Planar regionsinclude, for example, building facades where text is often located. Theplanar map is then compared with the candidate text regions detected bythe classifier. Because text lies substantially in a single plane,candidate text regions that are not planar can be eliminated asnon-text. Consequently, non-planar candidate text regions are eliminatedfrom further processing, reducing the number of false positive textresults. Furthermore, by constraining candidate text regions to planarregions, for example, to planar regions perpendicular to a cameraposition, other constraints can be relaxed such as the minimum sizerequirement for candidate text regions.

Additionally, in another implementation, the three-dimensional rangedata is used to focus the candidate text regions to particular types oftext of interest. For example, particular types of text in the image canbe targeted such as building names or street signs. The distanceprovided by the three-dimensional range data can be used to indicatedifferent types of image data such that distance based text detectioncriteria can be defined. If the camera and rang sensing equipmentmaintains substantially a same distance from the building facades as ittraverses a path down a street, then the three-dimensional rangeinformation can be used to locate candidate text regions of satisfyingparticular distance based criteria. Thus, when looking, for example, forbuilding identifiers (e.g., name, address number), the candidate textregions outside of a predetermined range criteria are eliminated (e.g.,removing foreground objects). Alternatively, in an implementation wherestreet signs are targeted for identification, a shorter range value isused to eliminate background candidate text regions.

Output from the text detection process can be provided in severalformats. In one implementation, the detected candidate text regions areoutlined within the image, as shown in FIG. 5A. Highlighting or othervisual cues can be used to distinguish the detected candidate textregions from the rest of the image. Additionally, the coordinates toeach candidate text region can be recorded to identify the candidatetext regions for subsequent processing as discussed below.Alternatively, a mask is generated for the image such that only thedetected text candidates are visible for further processing.

Candidate Text Enhancement

A number of factors can contribute to making the characters of imagetext difficult to identify. Image text can be small, blurred, havevarious distortions, or suffer from different artefacts, makingcharacter recognition difficult. Referring back to FIG. 3, followingtext detection, operations are performed to enhance the detectedcandidate text regions to improve the identification and extraction oftext within the candidate text regions (e.g., using the text boxenhancement component 208) (step 310). In one implementation, imageenhancement is provided by performing a superresolution process on eachcandidate text region within the image.

The superresolution process uses multiple images of a scene. Each imageincludes a version of a candidate text region representing the same textfrom the scene (e.g., several images of a scene from slightly differentperspectives). For images derived from film or from a high speed camerathat is moving relative to the target scene, multiple images aregenerated with slight variability due to the change in camera position.For example, a high speed camera taking images as a machine (e.g., amotorized vehicle) can traverse a street perpendicular to the targetstructures. The high speed camera can therefore capture a sequence ofimages slightly offset from each previous image according to the motionof the camera. Thus, by having multiple versions of a candidate textregion, the resolution of the candidate text region can be improvedusing the superresolution process. Additionally, a candidate text regionthat is partially obstructed from one camera position may reveal theobstructed text from a different camera position (e.g., text partiallyobscured by a tree branch from one camera position may be clear fromanother).

The detected candidate text regions from a number of images that includethe same text can be combined using the superresolution process toprovide an enhanced candidate text region. FIG. 6 is an example process600 for generating a superresolution image that provides an enhancedcandidate text region. A number of frames or consecutive images areextracted (step 602). The number of extracted images depends on thecapture rate of the camera as well as the number of images. Typically, agreater number of images leads to a higher quality superresolutionresult.

The candidate text regions from each extracted image are optionallyenlarged to compensate for text detection errors (step 604) (i.e., toinclude text which may extend beyond the candidate text region detectedby the classifier). FIG. 7A shows a set of similar images extracted forsuperresolution. Specifically, FIG. 7A shows a collection 700 ofslightly different images 702, 704, 706, 708, and 710, each imageincluding the same street sign for the street “LYTTON”.

The candidate text regions are scaled up, or supersampled, to a highresolution image (step 606). The high resolution scaling is performedusing bicubic splines; however, other scaling techniques can be used.FIG. 7B shows a scaled up version 712 of the text.

The candidate text regions for each image are positioned on a highresolution grid (step 608). FIG. 7C shows the supersampled text alignedto a high-resolution grid 714. The scaled up text from each image isaligned to the high-resolution grid such that the pixels of each imagematch (step 610). In one implementation, block matching (e.g.,hierarchical block matching) is used to align the pixels within the highresolution grid 714. Additionally, an interpolation process can beperformed in order to fill in any remaining grid pixels. The resultingaligned pixels are then combined to produce the superresolution image(step 612). For example, combining the pixels can include taking themedian value of each pixel for each image in the grid and combining thepixel values to produce the resultant superresolution image. FIG. 7Dshows a final superresolution image 716, which provides an enhancedimage version over the scaled image 712 shown in FIG. 7B.

Other processing can be performed on the candidate text regions toimprove the identification of any text within the regions. In oneimplementation, after extracting the images in step 602, the images arecorrected for perspective distortion. For example, a text sign can bepositioned at an angle relative to the camera, such that perspectivedistortion can interfere with the alignment of the images where theposition of the camera has changed between images.

Three-dimensional range data can also be used to align the images forperforming the superresolution process. For example, thethree-dimensional range data can identify a planar region at aparticular distance from the camera. A candidate text region can also beidentified at the same location. Using this information as well asknowledge of how much the camera position has moved between images, theexact location of the candidate text region can be calculated for asequence of images. Range and movement information can be used todetermine the processing necessary to properly align the images. Forexample, if the motion is small and the range is large, the motion canbe approximated to a simple translation. However, if the text is closeor the motion is large, more complex processing can be necessary.Additionally, the number of images used for superresolution processingcan be adjusted depending on the range of the text and the motion of thecamera (e.g., use more images when the text is close or motion is greatin order to compensate for the additional processing required).

In another implementation, additional normalization or image enhancementprocesses are performed or images can be upscaled without the multipleimages necessary for generating a superresolution image.

Text Identification

Referring back to FIG. 3, after enhancement, a character recognitionprocess is performed on the enhanced candidate text regions (e.g., usingcharacter recognition component 210) (step 312). In one implementation,the character recognition process is performed using an availablecharacter recognition application, for example, an optical characterrecognition (“OCR”) application. In an alternative implementation, acharacter recognition application is built specifically to identify textin images.

The character recognition component is provided with two versions ofeach enhanced candidate text region. The first version is the enhancedcandidate text region as generated above. The second version of theenhanced candidate text region is an inverted version. Since characterrecognition applications are typically designed to identify black texton a white background, providing an inverted version as well as theoriginal version compensates for the use of white text in the candidatetext regions (e.g., white lettering on a dark background).

FIG. 8A shows an image 800 including detected candidate text regions 802a-f. FIG. 8B shows corresponding character recognition results 804 forthe candidate text regions 802 a-f. In FIG. 8B, each of the detectedcandidate text region 802 a-f is shown with the superresolution resultand the supersampled result. Additionally, the text identified from eachcandidate text region by the character recognition process is displayed.For example, candidate text region 802 a (FIG. 8A) is shown with asimplified example superresolution version 806 and scaled up version 808(FIG. 8B). The two versions are provided as a comparison between theenhancement provided by the superresolution process and simply scalingthe candidate text region.

The superresolution version 812 is also shown for the candidate textregion 802 e. Candidate text region 802 e is the candidate text regionthat includes the building number “115”. The character recognitionprogram provided the correct result 814 from the superresolution version812. False text results are also identified, for example, the characterrecognition result 810 shows identified text from the candidate textregion 802 a as “00000”.

Following the character recognition process, further filtering can beperformed on the detected results in order to remove erroneouslyidentified text such as result 810. For example, the results can befiltered to remove nonsense results such as result 816 and non-wordresult 818 (“bifill”).

In one implementation, the character recognition process is constrainedaccording to values in a database. Database assisted characterrecognition is disclosed in commonly-owned co-pending U.S. patentapplication Ser. No. 11/305,694 filed on Dec. 16, 2005, and entitled“Database Assisted OCR for Street Scenes,” which is hereby incorporatedby reference.

In one implementation, the character recognition is constrained byparticular business names within a database. For instance, the characterrecognition process constrained to look for McDonalds, Fry'sElectronics, H&R Block, and Pizza Hut, within the images. The characterrecognition process can alternatively be constrained, for example, byidentifying the type of store or stores within a target address rangeknown for the image, for example, based on a directory listing (e.g.,“yellow pages” listing) for that address range (e.g., “bars andrestaurants” or “flowers”). In addition, text related to a particularsubject category can be obtained, for example, by accessing web sites ofstores in that category and adjusting the language model used forcharacter recognition, accordingly.

In one implementation, the constrained character recognition search iscarried out using a template matching technique. For instance, supposethat one of the candidate words being searched for in an image is “155”(i.e., the building address number). In this case, a number of bitmaprenditions of “155” are generated at various scales and using variousfonts. Then, image-based template matching techniques can be used tocompare the candidate text region with these various renditions.

In another implementation, the character recognition is constrained byusing a “digits only” lexicon or language pack. This limits the searchto street numbers only (or other numeric patterns), but because of theconstraint introduced, greater accuracy is achieved. In one suchembodiment, the image can be binarized using, for example, the Niblackapproach (e.g., Wayne Niblack, An Introduction to Image Processing,Prentice-Hall, Englewood Cliffs, N.J., 1986, pp. 115-116, which isherein incorporated in its entirety by reference), and then running acommercial character recognition application (e.g., Abbyy FineReaderwith a digits-only lexicon). Other such image processing techniques canbe used as well.

APPLICATIONS Indexing

The results of the text recognition can be indexed. The extracted imagetext is associated with the image, such that the image is identified andretrieved according to the indexed image text. Searching, mapping, orother applications can be used, for example, to provide particularimages to a user according to the results of particular user searchingcriteria.

In one implementation, the extracted text results from text recognitionof images derived from street scenes is indexed and associated with amapping application. A user of the mapping application can search for alocation, for example, by business name, address, store hours, or otherkeywords. In addition to mapping the location for the user, the mappingapplication can retrieve images matching the user's search. For example,a user enters a search for a McDonald' s in a particular city or near aparticular address. The mapping application generates a map to theMcDonald' s as well as presents an image of the McDonald's. TheMcDonald's image is retrieved using the indexed text from the imageidentifying the McDonald's and location information associated with theimage, which identifies the location of the particular McDonald's in theimage.

In another implementation, since the images are associated with locationdata, the mapping application also provides images of businesses locatednearby a searched location, as well as identifying the locations of thebusinesses on a map. For example a user searching for a particularlocation or business is provided with search results as well asadditional results associated with the location or business. Images ofthe destination location as well as the associated results are presentedto the user. Other information retrieved from the particular images canoptionally be presented to the user as well. For example, business hoursextracted from the image can be shown.

Additionally, images of similar business as a searched for business canbe presented to the user as alternatives. For example, a search for abusiness of one type can result in images being presented of nearbybusinesses according to the indexed image text results, providing theuser with additional options.

In one implementation, advertisements are presented along with thepresented image. For example, an advertisement can be presented for thebusiness identified in the image. Alternatively, one or moreadvertisements can be presented for alternative businesses.Additionally, the advertisement can be for one or more productsassociated with the business in the presented image, user search terms,or according to other criteria.

In addition to street scenes, indexing can be applied to other imagesets. In one implementation, a store (e.g., a grocery store or hardwarestore) is indexed. Images of items within the store are captured, forexample, using a small motorized vehicle or robot. The aisles of thestore are traversed and images of products are captured in a similarmanner as discussed above. Additionally, as discussed above, locationinformation is associated with each image. Text is extracted from theproduct images. In particular, extracted text can be filtered using aproduct name database in order to focus character recognition results onproduct names.

An application for searching stores provides a user with locationinformation for desired products. For example, a user inputs a searchfor a product, for example, by product name, category, or other searchcriteria. Matching results are presented to the user including locationinformation for each matching product within the store. Consequently,the user can quickly navigate the store to locate and obtain the desiredproduct. Additionally, in another implementation, a number of stores areindexed such that a user searching for a particular product can beprovided with the nearest store carrying the desired product in additionto the product's location within the store.

Similarly, in another implementation, an image set associated with oneor more museums is indexed. In museums, text associated with exhibits,artefacts, and other displays is often displayed. Images of museum itemsincluding the associated text displays are captured as discussed abovewith respect to indexing a store. As with the store example, locationinformation is associated with each captured image. The text isextracted from the images. Consequently, an application for searchingmuseums provides a user with location information for the variousexhibits, artefacts, and other displays in the museum. The user cansearch for a particular object or use keywords to identify objectsassociated with an area of interest (e.g., impressionist painting, Greekstatues). Alternatively, the user can browse a the museum to learn aboutthe various objects.

Image Searching

Extracted image text can be stored for use an in image searchapplication. Image search applications are used to retrieve and presentimages for users, for example, according to one or more search terms.Each image is associated with keyword search terms, for example, derivedfrom an image caption, image metadata, text within a predefinedproximity of the image, or manual input. Additionally, image searchapplication can include the text extracted from within the images toidentify keywords associated with the image. Thus, the text within theimage itself can be used as a search parameter.

A search can be initiated by a user providing one or more search termsto the search application. The search terms can be associated with oneor more particular keywords. Images associated with the keywords areretrieved and presented to the user.

In one implementation, a particular weighting is be applied to imagetext. For example, matches to image text can be given greater (orsmaller) weight in the search results over text within a caption orotherwise associated with the image, which can be misleading.Alternatively, image text can be used to filter search results toeliminate particular images from a search result according to one ormore predefined keywords (e.g., to reduce the retrieval of inappropriateimages, spam filtering, etc.).

One or more visual identifiers can be associated with the presentedimages. For example, the text within the images corresponding to theuser's search can be highlighted or visually identified in some othermanner (e.g., by underlining, etc.).

Additionally, in one implementation, the image is presented along withone or more advertisements. The advertisements can be selected based onthe content of one or more search terms provided by the user.

Embodiments of the invention and all of the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe invention can be implemented as one or more computer programproducts, i.e., one or more modules of computer program instructionsencoded on a computer-readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer-readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or morethem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer-readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, embodiments of the invention canbe implemented on a computer having a display device, e.g., a CRT(cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back-end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front-end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understand as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results.

What is claimed is:
 1. (canceled)
 2. A computer-implemented methodcomprising: receiving a plurality of different images of a first scene,wherein each image has a different exposure level; generating a highdynamic range image using the plurality images; detecting one or morefeatures in each of one or more regions of the high dynamic range image;determining for each region of the high dynamic range image whether theregion is a candidate text region potentially containing text based onthe detected one or more features; and generating text by performingoptical character recognition on a plurality of the regions determinedto contain text.
 3. The method of claim 2, further comprising:increasing contrast of the high dynamic range image includingnormalizing pixel values in the high dynamic range image.
 4. The methodof claim 3, wherein normalizing pixel values in the high dynamic rangeimage comprises: computing a mean and variance of pixel values in thehigh dynamic range image; and scaling pixel values in the high dynamicrange image according to the computed mean and variance.
 5. The methodof claim 2, further comprising: generating a superresolution version ofthe candidate text region.
 6. The method of claim 5, wherein generatingthe superresolution version of the candidate text region comprises:obtaining a plurality of versions of the candidate text region, eachversion obtained from a corresponding image of the plurality of images;aligning the versions of the candidate text region to a high resolutiongrid; and compositing the aligned versions of the candidate text regionto generate the superresolution version of the candidate text region. 7.The method of claim 6, further comprising: supersampling the obtainedversions of a particular candidate text region from each image of theplurality of images.
 8. The method of claim 6, wherein aligning theversions of the candidate text region comprises aligning pixels of theversions of the candidate text region using block matching.
 9. Themethod of claim 6, wherein aligning the versions of the candidate textregion comprises: receiving ranging data and movement informationassociated with each version of the candidate text region; and aligningthe versions of the candidate text region based at least in part on thereceived ranging data and movement information.
 10. The method of claim6, wherein compositing the aligned versions of the candidate text regioncomprises combining pixels from each version of the candidate textregion including: computing a median value of pixels in each version ofthe candidate region; and combining the computed median values forcorresponding pixels in the aligned versions of the candidate textregion.
 11. A system, comprising: one or more processing devices; and amemory storage apparatus in data communication with the one or moreprocessing devices and storing instructions that cause the one or moreprocessing devices to perform operations comprising: receiving aplurality of different images of a first scene, wherein each image has adifferent exposure level; generating a high dynamic range image usingthe plurality images; detecting one or more features in each of one ormore regions of the high dynamic range image; determining for eachregion of the high dynamic range image whether the region is a candidatetext region potentially containing text based on the detected one ormore features; and generating text by performing optical characterrecognition on a plurality of the regions determined to contain text.12. The system of claim 11, the operations further comprising:increasing contrast of the high dynamic range image includingnormalizing pixel values in the high dynamic range image.
 13. The systemof claim 12, wherein normalizing pixel values in the high dynamic rangeimage comprises: computing a mean and variance of pixel values in thehigh dynamic range image; and scaling pixel values in the high dynamicrange image according to the computed mean and variance.
 14. The systemof claim 11, the operations further comprising: generating asuperresolution version of the candidate text region.
 15. The system ofclaim 14, wherein generating the superresolution version of thecandidate text region comprises: obtaining a plurality of versions ofthe candidate text region, each version obtained from a correspondingimage of the plurality of images; aligning the versions of the candidatetext region to a high resolution grid; and compositing the alignedversions of the candidate text region to generate the superresolutionversion of the candidate text region.
 16. The system of claim 15, theoperations further comprising: supersampling the obtained versions of aparticular candidate text region from each image of the plurality ofimages.
 17. The system of claim 15, wherein aligning the versions of thecandidate text region comprises aligning pixels of the versions of thecandidate text region using block matching.
 18. The system of claim 15,wherein aligning the versions of the candidate text region comprises:receiving ranging data and movement information associated with eachversion of the candidate text region; and aligning the versions of thecandidate text region based at least in part on the received rangingdata and movement information.
 19. The system of claim 15, whereincompositing the aligned versions of the candidate text region comprisescombining pixels from each version of the candidate text regionincluding: computing a median value of pixels in each version of thecandidate region; and combining the computed median values forcorresponding pixels in the aligned versions of the candidate textregion.
 20. A memory storage apparatus storing instructions that causethe one or more processing devices to perform operations comprising:receiving a plurality of different images of a first scene, wherein eachimage has a different exposure level; generating a high dynamic rangeimage using the plurality images; detecting one or more features in eachof one or more regions of the high dynamic range image; determining foreach region of the high dynamic range image whether the region is acandidate text region potentially containing text based on the detectedone or more features; and generating text by performing opticalcharacter recognition on a plurality of the regions determined tocontain text.